Promoting cultural rigour through critical appraisal tools in First Nations peoples’ research
Bibliographic record
Abstract
OBJECTIVE: To highlight the emerging ethos of cultural rigour in the use of critical appraisal tools in research involving First Nations peoples. METHODS: Critical reflection on recent systematic review experience. RESULTS: The concept of cultural rigour is notably undefined in peer-reviewed journal articles but is evident in the development of critical appraisal tools developed by First Nations peoples. CONCLUSIONS: Conventional critical appraisal tools for assessing study quality are built on a limited view of health that excludes the cultural knowledge of First Nations peoples. Cultural rigour is an emerging field of activity that epitomises First Nations peoples' diverse cultural knowledge through community participation in all aspects of research. Implications for public health: Critical appraisal tools developed by First Nations peoples are available to researchers and direct attention to the social, cultural, political and human rights basis of health research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.845 | 0.900 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.035 | 0.020 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.033 | 0.025 |
| Open science | 0.009 | 0.024 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".